Qwen3 VL 32B Instruct
qwen3-vl-32b-instructQwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text... Built by Qwen.
Prices updated
Input price
$0.10
per 1M tokens · Standard
Output price
$0.42
per 1M tokens · Standard
Input limit
131K
tokens
Output limit
33K
tokens
Input formats
Output formats
Qwen3 VL 32B Instruct price history
2 price records since 9/25/2026
Qwen3 VL 32B Instruct cost calculator
$4.16/month
Standard pricing
Overview
What is Qwen3 VL 32B Instruct?
Qwen3 VL 32B Instruct is a text & reasoning and vision model from Qwen. Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text... Its 131K context window and $0.10 input price make it a candidate for cost-sensitive, high-throughput applications.
Benchmarks
Qwen3 VL 32B Instruct benchmarks & speed
How Qwen3 VL 32B Instruct scores on standardized evaluations, and where it lands among every model we track.
8.4
Intelligence Index
62tok/s
Output speed
Reasoning
GPQA Diamond
67.1%
Graduate-level scientific reasoning · top 73%
HLE
6.8%
Humanity's Last Exam · top 71%
Latency & design
- Time to first token
- 2.71s
Independent scores from Artificial Analysis and DesignArena · updated 10/5/2026. Higher is better; ranks compare against every model we track with that score.
Rates
Qwen3 VL 32B Instruct pricing
Pricing is based on token usage. Provider-specific caching, batch, regional, and tool charges may affect the final cost.
Input · Standard
$0.10
Per 1M tokens
Cached input · Standard
Not available
No listed cached-input rate
Capabilities
Qwen3 VL 32B Instruct Tools
Tools available when using Qwen3 VL 32B Instruct through supported provider APIs.
Function calling
Supported
Structured outputs
Supported
JSON mode
Supported
Reasoning
Not supported
Built-in web search
Not supported
Log probabilities
Supported
Deterministic seed
Supported
Parallel tool calls
Not supported
Prompt caching
Not supported
Strengths and limitations
Strengths
- Low input cost at $0.10 per million tokens suits high-volume workloads.
- A 131K context window covers most focused application workflows.
- Supports text & reasoning and vision workloads in one model.
Limitations
- Generated tokens cost 4× more than input tokens, which matters for verbose responses.
- The 131K context window is smaller than several long-context alternatives.
- Arena Elo and MMLU-Pro are directional; test accuracy, latency and reliability on your own workload before committing.
Arena Elo, MMLU-Pro and pricing figures are illustrative; validate current vendor terms before purchase.
Same provider